skj dZddlmZddlZddlZddlZddlmZddlm Z dZ ejdZ d jd e jDZd ed Zd dddddddddidddidgdddZd)dZd*d+dZd,dZd-dZedk(rddlmZGdd ZeZejDcic]\}}||ec}}Zej6eeegZeed!eek(s Jed!ed"ed!d#d$DsJejred%dusJed&e ejBed'e"eed(eyycc}}w).uAI sentiment read on a single ticker — advisory only, feeds no score. Pulls recent headlines from Yahoo (via yfinance), hands them to Claude together with every strategy signal the screener already computed, and gets back a sentiment score plus a reconciliation of news against numbers. Deliberately standalone: nothing here is called from engine.py or data.py, so a screen stays offline-fast. See SCORING_SEAM at the bottom before changing that. ) annotationsN)asdict) DESCRIPTIONSz claude-opus-5z<[^>]+> c#2K|]\}}d|d|yw)z- z: N).0namedescs screener/sentiment.py rs!OJD$bb'Osz~You are an equity-research assistant for an educational stock screener. The screener scores every stock through four lenses: u Each lens is a weighted mean of individual signals. Read the payload carefully: - `signals[].score` is 0..1. `strategies[].score` and `verdict` are 0..100. - A signal `score` of null means the metric was MISSING from Yahoo's data and was EXCLUDED from the mean. It was NOT scored neutrally. Never treat null as 0.5, and never read a null as a bad result — it is an absent one. - `signals[].note` explains what the metric is meant to capture. `headlines` is Yahoo Finance's publisher feed for this ticker. It is a narrow source, not a survey of the whole internet, and be aware of two things: - Some items will be about OTHER companies (peers, suppliers, sector stories). Ignore those. Do not attribute them to this company. - Ten headlines is not a sentiment survey. If coverage is thin, sparse, or stale, say so plainly and keep your score near neutral. Never invent sentiment or imply a consensus you cannot see. Absence of news is not bullish or bearish. Your job is to reconcile what the press says against what the numbers say. Return: - `score`: 0-100 sentiment. 0 = maximally bearish, 50 = neutral, 100 = maximally bullish. Judge the mood of the coverage against the fundamentals, and stay near 50 when evidence is weak or conflicting. - `verdict`: two to four words, e.g. "Cautiously bearish". - `bullets`: 3-6 items. Each must name a SPECIFIC metric from the payload by its exact signal name (e.g. ROE, ShortFloat%, MeanRev_Z, EV/EBITDA) and say whether the news agrees with it, contradicts it, or is silent on it. Cite the publisher when you lean on a headline. Where the news and the metric disagree, say so — that tension is the most useful thing you can surface. - `suggestion`: one short paragraph on what a reader should watch, verify, or investigate next, and which single number would most change the picture. Educational framing only. Never tell the reader to buy, sell, or hold, and never predict a price. Be concise and concrete: no preamble, no restating the payload, no hedging boilerplate.objectintegerz0-100 sentiment, 50 = neutral)type descriptionstringzTwo to four word summaryarrayr)ritemsscoreverdictbullets suggestionF)r propertiesrequiredadditionalPropertiescRttjjdS)zTrue when an API key is reachable. Streamlit promotes every top-level key in .streamlit/secrets.toml into os.environ at server start, so an env var and a secrets file both land here. ANTHROPIC_API_KEY)boolosenvirongetr r availabler%Ls  23 44r$c  ddl}|j|jxsg}g}|d|D]1}|j dxs|xsi}|j dxsdj }|sC|j dxs|j dxsdj }|j dxs|j d xsij d } |j |tjd|dd |j d xsij d |j dxs|j d|j d| d4|S#t$rgcYSwxYw)a%Recent Yahoo headlines for a ticker, flattened. Never raises. yfinance >= 1.x nests each item as {"id": ..., "content": {...}}; older 0.2.x releases returned flat dicts. Read defensively so a shape change degrades to an empty list rather than silently yielding blank strings. rNcontenttitlesummaryr canonicalUrlclickThroughUrlurliXprovider displayNamepubDate displayTime contentType)r(r* publisher publishedkindr-) yfinanceTickernews Exceptionr#stripappend_TAGSsub) tickerlimityfroutitemcr(r*r-s r headlinesrDUsZ &!&&," Cfu   HHY  -4 -2w%2,,. 55#Aquu]';ArHHJuu^$F.?(@FBKKER yyW-ds3%% +1r66}Ey)AQUU=-AEE-(     J'  s#E## E10E1ctj|j|j|j|j |j |jDcic]\}}|t|c}}|dtScc}}w)u3Serialise ticker + every StrategyResult/Signal + headlines to JSON. Returns a str, so it is hashable and usable directly as a cache key. StrategyResult and Signal are plain dataclasses, so asdict() is the whole encoder. StockData itself is never passed through it — it holds a DataFrame. )r>r sectorprice market_cap strategiesrD)default) jsondumpsr>r rFrGrHrrstr)sdresultsr8r ress r payloadrQvsl ::iiGGiiXX-->EmmoNs4,N   OsBc (ddl}|jjjtdt ddiddt dd d |d g }|jd k(rdddgddStd|jD}tj|S)uClaude's sentiment read. Returns keys: score, verdict, bullets, suggestion. Raises on network/API failure — callers should surface that, not swallow it. rNi>radaptivemedium json_schema)rschema)effortformatuser)roler')model max_tokenssystemthinking output_configmessagesrefusal2zNo read availablez(Claude declined to analyse this request.zTry a different ticker.rc3TK|] }|jdk(s|j"yw)textN)rrd)r bs r rzanalyze..s>1QVVv-=>s(() anthropic Anthropicr`createMODELSYSTEMSCHEMA stop_reasonnextr'rKloads) payload_jsonrfrrds r analyzerqs &&--*%,? "l;< . A }} !*BC3   > > >D ::d r$__main__) STRATEGIESc\eZdZUd\ZZZZZdddZde d<e dZ e dZ d Z y ) _Fake)TESTzTest CoTechg$@geAg?g(@)returnOnEquity trailingPEdictinfoc@tjj|S)N)rur{r#)ks r z_Fake.sUZZ^^A%6r$cy)Ng?r )ds r r~z_Fake.sr$c6ddl}|j|_y)Nr)pandas DataFramehistory)selfpds r __init__z_Fake.__init__s <<>DLr$N)__name__ __module__ __qualname__r>r rFrGrHr{__annotations__ staticmethodr#returnsrr r$r rurus;2V/feZ(+4@d@67}- *r$rurIc#,K|] }|ddk(yw)r ROENr )r ss r rrsQaqyE!QsBuffettsignalsrupayload OK — z chars, key set zapi key present: )returnr ) )r>rMr?intr list[dict])rOrzr8rrrM)rorMrrz)#__doc__ __future__rrKr!re dataclassesrrIrricompiler<joinr_LENSESrjrkr%rDrQrqrrsrurNrOrnpsetanyprintlenrLsorted)nfs00r rs# $ : ))O:L,:L:L:NO O " % P #4ST$5OP#vx.@Ax(  >! 5B*R z&** B$4J$4$4$67DAqq!B%x7G 72w+,A q 3z? 2CAlOC 2 Q1\?9+Ei+PQ QQ Q   (>!?5!HH H OC  1 .//?q { KL ik] +,)8s>E8